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import torch
import os
import asyncio
import requests
from io import BytesIO
from PIL import Image
from urllib.parse import urlparse
import numpy as np
def split_image_ur(img, max_slice_num, image_size, vit_image_size, force_min_size=False):
if force_min_size:
img = resize_by_patch_size_ur(img, min_size= image_size, max_size= image_size * max_slice_num, patch_size=14)
slice_config = {
"max_slice_nums": max_slice_num,
"scale_resolution": image_size,
"patch_size": 14
}
source_image, sub_images, _ = do_slice_by_minicpmv_strategy_ur(
img, max_slice_nums=slice_config["max_slice_nums"], scale_resolution=slice_config["scale_resolution"], patch_size=slice_config["patch_size"], vit_image_size=vit_image_size)
splits = []
splits.append(source_image)
for i in range(len(sub_images)):
for j in range(len(sub_images[0])):
splits.append(sub_images[i][j])
sliced_images, sliced_shapes = [], []
for slice_image in splits:
sliced_images.append(slice_image)
sliced_shapes.append(np.array((slice_image.size[0] // slice_config["patch_size"], slice_image.size[1] // slice_config["patch_size"])))
return sliced_images, sliced_shapes
import math
from PIL import Image
import torch
import torchvision.transforms.functional as F
from torchvision.transforms import InterpolationMode
# Strategy: MiniCPM-V
def do_slice_by_minicpmv_strategy_ur(image, max_slice_nums=9, scale_resolution=1120, patch_size=14, vit_image_size=448, never_split=False):
original_size = image.size
original_width, original_height = original_size
log_ratio = math.log(original_width / original_height)
ratio = original_width * original_height / (scale_resolution * scale_resolution)
multiple = min(math.ceil(ratio), max_slice_nums)
source_image = None
best_grid = None
patches = []
if multiple <= 1 or never_split:
# dont need to slice, upsample
# best_size = find_best_resize(
# original_size, scale_resolution, patch_size, allow_upscale=True
# )
best_size = (scale_resolution, scale_resolution)
source_image = image.resize(best_size, Image.BICUBIC)
border_size = (vit_image_size-scale_resolution)/2
from PIL import ImageOps
source_image = ImageOps.expand(source_image, border=int(border_size), fill=(0,0,0))
else:
candidate_split_grids_nums = []
for i in [multiple - 1, multiple, multiple + 1]:
if i == 1 or i > max_slice_nums:
continue
candidate_split_grids_nums.append(i)
# source image, down-sampling and ensure divided by patch_size
# best_resize = find_best_resize(original_size, scale_resolution, patch_size)
# source_image = image.copy().resize(best_resize, Image.BICUBIC)
source_image = image.copy().resize((scale_resolution,scale_resolution), Image.BICUBIC)
candidate_grids = []
# find best grid
for split_grids_nums in candidate_split_grids_nums:
m = 1
while m <= split_grids_nums:
if split_grids_nums % m == 0:
candidate_grids.append([m, split_grids_nums // m])
m += 1
# print("candidate_grids: ", candidate_grids)
best_grid = [1, 1]
min_error = float("inf")
for grid in candidate_grids:
error = abs(log_ratio - math.log(grid[0] / grid[1]))
if error < min_error:
best_grid = grid
min_error = error
refine_size = get_refine_size(
original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
)
refine_image = image.resize(refine_size, Image.BICUBIC)
patches = split_to_patches(refine_image, best_grid, scale_resolution, vit_image_size)
return source_image, patches, best_grid
def ensure_divide(length, patch_size):
return max(round(length / patch_size) * patch_size, patch_size)
def find_best_resize(original_size, scale_resolution, patch_size, allow_upscale=False):
width, height = original_size
if (width * height > scale_resolution * scale_resolution) or allow_upscale:
r = width / height
height = int(scale_resolution / math.sqrt(r))
width = int(height * r)
best_width = ensure_divide(width, patch_size)
best_height = ensure_divide(height, patch_size)
# print(best_width, best_height, scale_resolution)
while best_width * best_height > scale_resolution ** 2:
# print(best_width)
best_width -= patch_size
return (best_width, best_height)
def get_refine_size(original_size, grid, scale_resolution, patch_size, allow_upscale=False):
width, height = original_size
grid_x, grid_y = grid
# refine_width = ensure_divide(width, grid_x)
# refine_height = ensure_divide(height, grid_y)
# grid_width = refine_width / grid_x
# grid_height = refine_height / grid_y
# best_grid_size = find_best_resize(
# (grid_width, grid_height),
# scale_resolution,
# patch_size,
# allow_upscale=allow_upscale,
# )
refine_size = (scale_resolution * grid_x, scale_resolution * grid_y)
return refine_size
def split_to_patches(image, grid, scale_resolution, vit_image_size):
patches = []
width, height = image.size
grid_x = int(width / grid[0])
grid_y = int(height / grid[1])
from PIL import ImageOps
border_size = (vit_image_size - scale_resolution)/2
padded_img = ImageOps.expand(image, border=int(border_size), fill=(0,0,0))
padded_width, padded_height = padded_img.size
for i in range(0, padded_height-vit_image_size+1, scale_resolution):
images = []
for j in range(0, padded_width-vit_image_size+1, scale_resolution):
box = (j, i, j + vit_image_size, i + vit_image_size)
patch = padded_img.crop(box)
images.append(patch)
patches.append(images)
return patches
def resize_by_patch_size_ur(img, min_size=1152, max_size=2240, patch_size=14):
interpolation=InterpolationMode.BICUBIC
# min_size=756, max_size=756 * 4, patch_size=14
if isinstance(img, torch.Tensor):
height, width = img.shape[:2]
else:
width, height = img.size
# Check if the shorter side is less than min_size
if min(height, width) < min_size:
# print('less than min_size')
scale_factor = min_size / min(height, width)
new_height = max(min_size, round(height * scale_factor))
new_width = max(min_size, round(width * scale_factor))
# print(self.max_size)
# Check if the longer side after resizing is greater than max_size
if max(new_height, new_width) > max_size:
scale_factor = max_size / max(new_height, new_width)
new_height = min(max_size, round(new_height * scale_factor))
new_width = min(max_size, round(new_width * scale_factor))
else:
scale_factor = min(max_size / max(height, width), 1)
new_height = round(height * scale_factor)
new_width = round(width * scale_factor)
# # Make sure the new height and width are divisible by patch_size
# new_height = (new_height // patch_size) * patch_size
# new_width = (new_width // patch_size) * patch_size
# Resize the image
# img = F.resize(img, (new_height, new_width), interpolation)
img = img.resize((new_width, new_height), Image.BICUBIC)
return img |